Time series of long-term annual fluxes in the streamwater of nine forest catchments from the Swedish environmental monitoring program (PMK 5)

2003 ◽  
Vol 310 (1-3) ◽  
pp. 113-120 ◽  
Author(s):  
Jens Fölster ◽  
Kevin Bishop ◽  
Pavel Krám ◽  
Hans Kvarnäs ◽  
Anders Wilander
Keyword(s):  
Time Series ◽  
Environmental Monitoring ◽  
Monitoring Program

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

scholarly journals The discontinuity of environmental effects monitoring in the Lower Athabasca region of Alberta, Canada: institutional challenges to long-term monitoring and cumulative effects management

2018 ◽  
Vol 26 (2) ◽  
pp. 169-180 ◽  
Author(s):  
Joshua G. Cronmiller ◽  
Bram F. Noble
Keyword(s):  
Decision Making ◽  
Environmental Monitoring ◽  
Environmental Effects ◽  
Regional Scale ◽  
Monitoring Program ◽  
Institutional Arrangements ◽  
Cumulative Effects ◽  
Regulatory Decision ◽  
Monitoring Programs

Long-term regional environmental monitoring, coupled with shorter-term and more localized monitoring carried out under regulatory permitting processes, is foundational to identifying, understanding, and effectively managing cumulative environmental effects. However, monitoring programs that emerge to support cumulative effects science are often short-lived initiatives or disconnected from land use planning and regulatory decision making. This paper examines the history and evolution of environmental monitoring in the Lower Athabasca region of Alberta, Canada, and the enabling and constraining influences of institutional arrangements. Methods involved a review of regional-scale monitoring programs based on an analysis of monitoring agency mandates, performance reports, and external program reviews, supplemented by discussions with monitoring program or agency key informants to triangulate results. Results show that monitoring to support cumulative effects understanding in the Lower Athabasca has advanced considerably, especially since the mid-1990s, but its relevance to, and impact on, cumulative effects management and decision making has been stifled by institutional arrangements. Monitoring has been episodic, reflecting shifting priorities and competing mandates; criticized by stakeholders based on concerns about transparency, credibility, influence over decision making; and characterized by short-lived commitments by the agencies involved. This has generated significant uncertainty about the stability of institutional arrangements to support long-term environmental monitoring, and tensions between the need for scientific autonomy for credible science whilst ensuring the pursuit of monitoring questions that are relevant to the day-to-day needs of regulatory decision makers. Regional monitoring programs require, at a minimum, clear vision and agreed-upon monitoring questions that are of scientific and management value, meaningful and balanced stakeholder engagement, and a clear governance process to ensure credibility and influence of monitoring results on decision making.

Using Time Series to Incorporate Seasonal Variations in Pavement Design

1996 ◽  
Vol 1539 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Hesham A. Ali ◽  
Neville A. Parker
Keyword(s):  
Time Series ◽  
Structural Properties ◽  
Seasonal Variations ◽  
Monitoring Program ◽  
Statistical Technique ◽  
Pavement Design ◽  
Seasonal Monitoring ◽  
Program Data ◽  
Series Technique

Analysis of the seasonal monitoring program data of the long term pavement performance program indicated that some pavement structural properties often follow predictable seasonal patterns. Time series is a statistical technique that may be used to develop periodic functions to predict the values of such properties as a function of time. The application of time series technique in characterizing the seasonal variations of pavement structural properties as simulated functions is presented. In addition, the incorporation of such variations in both empirical and mechanistic-empirical methods of flexible pavement design is demonstrated. To this end, a computer program, seasonal variation in pavement design, was written to carry out the required calculations and to facilitate the comparison between empirical and mechanistic-empirical design methods.

scholarly journals EVOS and the Prince William Sound Long Term Environmental Monitoring Program

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
James R. Payne ◽  
William B. Driskell ◽  
David Janka ◽  
Lisa Ka'aihue ◽  
Joe Banta ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Ballast Water ◽  
Monitoring Program ◽  
Gulf Of Alaska ◽  
Advisory Council ◽  
Prince William Sound ◽  
Mussel Tissue ◽  
Weathered Oil ◽  
Diesel Spill

ABSTRACT Following the 1989 Exxon Valdez oil spill (EVOS), the Prince William Sound Regional Citizens' Advisory Council began the Long-Term Environmental Monitoring Program (LTEMP) in 1993 to track oil hydrocarbon chemistry of recovering sediments and mussel tissues along the path of the spill in Prince William Sound (PWS) and across the Northern Gulf of Alaska (NGOA) region. The program also samples sites near the Alyeska Marine Terminal (AMT) within Port Valdez, primarily to monitor tanker operations and the resulting treatment and discharge of oil-contaminated tanker ballast water. Over the last 28 years, the program has documented EVOS oil's disappearance at the spill-impacted sites (albeit buried oil still exists at a few unique sheltered locations in PWS). Within the Port, a few tanker- and diesel-spill incidents have been documented over the years, but all were minor and with recovery times of < 1 yr. Of highest concern has been the permitted chronic release of weathered oil from tankers' ballast-water that is treated and discharged at the Alyeska Marine Terminal (AMT). In earlier years (1980s–90s), with discharge volumes reaching 17–18 MGD, up to a barrel of finely dispersed weathered oil would be released into the fjord daily. Over the last two decades, total petrogenic inputs (TPAH43) into the Port have declined as measured in the monitored mussels and sediments. This trend reflects a combination of decreased Alaska North Slope (ANS) oil production and thus, less tanker traffic, plus less ballast from the transition to double-hulled tankers with segregated ballast tanks, and improved treatment-facility efficiency in removing PAH. From the 2018 collections, mussel-tissue hydrocarbon concentrations from all eleven LTEMP stations (within Port Valdez as well as PWS and NGOA regions) were below method detection limits and similar to laboratory blanks (TPAH43 < 44 ng/g dry wt.). At these low background levels, elevated TPAH values from a minor 2020 spill incident at the Terminal were easily detected at all three Port Valdez stations.

Mearns Rock: Twenty-six Years and Twenty-six photos that Put a Human Dimension on Long term Recovery Following the Exxon Valdez Oil Spill

2017 ◽  
Vol 2017 (1) ◽  
pp. 2017062
Author(s):  
Alan Mearns
Keyword(s):  
Time Series ◽  
Web Sites ◽  
Monitoring Program ◽  
Remote Site ◽  
Quarter Century ◽  
Marine Life ◽  
The Past ◽  
Human Scale ◽  
Boom And Bust

During the course of a ten-year detailed shoreline monitoring program in Prince William Sound it became evident that the author's annually repeated, human-scale time-series color photos of recovering shoreline sites inspired appreciation of the variability of shoreline marine life on the part of managers, citizens and educators. One of these sites is “Mearns Rock”, a segment of an oiled “set-aside” shoreline centered on a human-sized boulder. With the help of staff and citizen volunteers, this remote site has been photographed every summer for 26 years yielding a human-scale view of dramatic year-to-year changes in the abundance of mussels, seaweeds and other inter-tidal marine life. In the years following oiling in 1989, seaweeds and mussels flourished, but a few years later the oil was gone and the scene was barren of life: a few years after that, life again flourished. Indeed, life on “the Rock” has undergone four major episodes of boom and bust during the past quarter century, testifying to the huge variability that would otherwise not be noticed by people other than scientists and their time series graphics buried in reports. In this paper I present the inspiration for the continued photo-monitoring, the contributions by volunteers, the photos and associated graphics, and especially examples of how the photo-series has been used in web-sites, films and books to inspire students, educators, staff and managers to learn more about variability of marine life and how difficult it is to determine when an injured resource has “recovered”. An accompanying poster will offer details about the photo-monitoring at this and eight other sites in the Sound.

scholarly journals Periodic variations in 6.7-GHz methanol masers

2007 ◽  
Vol 3 (S242) ◽  
pp. 97-101 ◽  
Author(s):  
Sharmila Goedhart ◽  
Michael J. Gaylard ◽  
D. Johan van der Walt
Keyword(s):  
Time Series ◽  
Radio Astronomy ◽  
Monitoring Program ◽  
Astronomy Observatory ◽  
Intensive Monitoring ◽  
Radio Astronomy Observatory ◽  
Long Term Trends ◽  
Methanol Masers ◽  
Maser Sources

AbstractAn intensive monitoring program of 54 6.7-GHz methanol maser sources was carried out at the Hartebeesthoek Radio Astronomy Observatory from January 1999 to April 2003. The monitoring program was subsequently continued on 19 sources of interest. Analysis of the resulting time-series stretching over eight years shows that six of the sources are periodic, with periods ranging from 133 days to 504 days. The waveforms in individual sources range from sinusoidal fluctuations to sharp flares and there can be other long term trends in the time-series. The amplitudes of the variations can also change from cycle to cycle. The time-series of the periodic masers will be presented, and possible causes of the variability discussed.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? I determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, I use simulation methods and examine 822 populations of vertebrate species. Here I show that on average 15.9 years of continuous monitoring are required in order to achieve a high level of statistical power. However, there is a wide distribution around this average, casting doubt on simple rules of thumb. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. However, there were no life-history traits (e.g. generation length) that were predictive of the minimum time required. These results point to the importance of sampling populations over long periods of time. I argue that statistical power needs to be considered in monitoring program design and evaluation. Short time series are likely under-powered and potentially misleading.

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